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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Photchara Ratsamee Yasushi Mae Amornched Jinda-apiraksa Jana Machajdik Kenichi Ohara Masaru Kojima Robert Sablatnig Tatsuo Arai Faculty of Systems Science and Applied Informatics at Osaka University Japan. Advanced Digital Sciences Center (ADSC) University of Illinois at Urbana-Champaign Singapore. Computer Vision Lab Institute of Computer Aided Automation Vienna University of Technology (TU Wien) Austria. Faculty of Science and Technology Meijo University Japan.

ISBN: (纸本)9781467363563

Keyframe selection is the process of finding a representative frame in an image sequence. Although mostly known from video processing, keyframe selection faces new challenges in the lifelog domain. To obtain a keyframe that is close to a user-selected frame, we propose a keyframe selection method based on image quality measurements and excitement features. Image quality measurements such as contrast, color variance, sharpness, noise and saliency are used to filter high quality images. However, high quality images are not necessarily keyframes because humans also use emotions in the selection process. In this study, we employ a biosensor to measure the excitement of humans. In previous investigation, keyframe selection using only image quality measurements yielded an acceptance rate of 79.70%. Our proposed method achieves an acceptance rate of 84.45%.

关键词： Quality Measurements Physiological Excitement Features lifelog domain Quality management excitement Image quality image sequences Process selection Human Emotions

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Text and data mining for biomedical discovery 18

Text and data mining for biomedical discovery

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18th Pacific Symposium on Biocomputing, PSB 2013

作者： Gonzalez, Graciela Cohen, Kevin Bretonnel Kann, Maricel G. Greene, Casey S. Leaman, Robert Hahn, Udo Shah, Nigam Ye, Jieping Department of Biomedical Informatics Arizona State University ScottsdaleAZ85259 United States Computational Bioscience Program U. Colorado School of Medicine AuroraCO United States Department of Biological Science University of Maryland Baltimore County BaltimoreMD21250 United States Department of Genetics Geisel School of Medicine at Dartmouth HanoverNH03755 United States National Center for Biotechnology Information BethesdaMD20894 United States Friedricdh Schiller University Jena Language and Information Engineering Lab. Jena Germany Stanford Center for Biomedical Informatics Research StanfordCA94305 United States Computer Science and Engineering Arizona State University TempeAZ85287 United States

ISBN: (纸本)9781627480161

The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health related terms to integrate consumer-friendly synonyms for medical terminologies.

关键词： Data mining

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A Multi-Channel Salience Based Detail Exaggeration Technique for 3D Relief Surfaces

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Journal of computer science & Technology 2012年第6期27卷 1100-1109页

作者：缪永伟冯结青王金荣 Renato Pajarola College of Computer Science and Technology Zhejiang University of Technology State Key Lab of CAD & CG Zhejiang University Department of Informatics University of Zürich

Visual saliency can always persuade the viewer＇s visual attention to fine-scale mesostructure of 3D complex shapes. Owing to the multi-channel salience measure and salience-domain shape modeling technique, a novel visual saliency based shape depiction scheme is presented to exaggerate salient geometric details of the underlying relief surface. Our multi-channel salience measure is calculated by combining three feature maps, i.e., the 0-order feature map of local height distribution, the l-order feature map of normal difference, and the 2-order feature map of mean curvature variation. The original relief surface is firstly manipulated by a salience-domain enhancement function, and the detail exaggeration surface can then be obtained by adjusting the surface normals of the original surface as the corresponding final normals of the manipulated surface. The advantage of our detail exaggeration technique is that it can adaptively alter the shading of the original shape to reveal visually salient features whilst keeping the desired appearance unimpaired. The experimental results demonstrate that our non-photorealistic shading scheme can enhance the surface mesostructure effectively and thus improving the shape depiction of the relief surfaces.

关键词： nmlti-channel salience salience-domain shape modeling detail exaggeration shape depiction relief surface

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A literature review of sensor ontologies for manufacturing applications

A literature review of sensor ontologies for manufacturing a...

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International Workshop on Robot Sensing (ROSE)

作者： Craig Schlenoff Tsai Hong Connie Liu Roger Eastman Sebti Foufou Intelligent Systems Division National Institute of Standards and Technology Gaithersburg MD USA Department of Computer Science Loyola College of Maryland Baltimore MD USA Dept. of Biomedical Informatics Columbia University New York NY USA LE2i Lab University of Burgundy Dijon France

The purpose of this paper is to review existing sensor and sensor network ontologies to understand whether they can be reused as a basis for a manufacturing perception sensor ontology, or if the existing ontologies hold lessons for the development of a new ontology. We develop an initial set of requirements that should apply to a manufacturing perception sensor ontology. These initial requirements are used in reviewing selected existing sensor ontologies. Additionally, we present our developed sensor ontology thus far that incorporates a refined list of requirements. This paper describes 1) extending and refining the requirements; 2) proposing hierarchical structures for verifying the purposes of the ontology; 3) choosing appropriate tools and languages (e.g., OWL (Web Ontology Language) [1] and SensorML (Sensor Markup Language) [2]) to support such an ontology; and 4) extending the Semantic Sensor Network (SSN) ontology with a set of refined requirements (i.e., our developed ontology). This work will be proposed as a standard within the IEEE (Institute of Electrical and Electronics Engineers) Robotics and Automation Society (RAS) Ontologies for Robotics Automation (ORA) Working Group [3].

关键词： Ontologies Robot sensing systems Manufacturing NIST OWL Semantics

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On the complexity of package recommendation problems 12

On the complexity of package recommendation problems

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31st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS '12

作者： Deng, Ting Fan, Wenfei Geerts, Floris School of Computer Science and Engineering Beihang University Beijing China Lab. for Foundations of Computer Science School of Informatics University of Edinburgh Edinburgh United Kingdom Dept. of Mathematics and Computer Science University of Antwerp Antwerp Belgium

ISBN: (纸本)9781450312486

Recommendation systems aim to recommend items that are likely to be of interest to users. This paper investigates several issues fundamental to such systems. (1) We model recommendation systems for packages of items. We use queries to specify multi-criteria for item selections and express compatibility constraints on items in a package, and use functions to compute the cost and usefulness of items to a user. (2) We study recommendations of points of interest, to suggest top-k packages. We also investigate recommendations of top-k items, as a special case. In addition, when sensible suggestions cannot be found, we propose query relaxation recommendations to help users revise their selection criteria, or adjustment recommendations to guide vendors to modify their item collections. (3) We identify several problems, to decide whether a set of packages makes a top-k recommendation, whether a rating bound is maximum for selecting top-k packages, whether we can relax the selection query to find packages that users want, and whether we can update a bounded number of items such that the users' requirements can be satisfied. We also study function problems for computing top-k packages, and counting problems to find how many packages meet the user's criteria. (4) We establish the upper and lower bounds of these problems, all matching, for combined and data complexity. These results reveal the impact of variable sizes of packages, the presence of compatibility constraints, as well as a variety of query languages for specifying selection criteria and compatibility constraints, on the analyses of these problems. © 2012 ACM.

关键词： Recommender systems

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Learning neighborhoods for metric learning

Learning neighborhoods for metric learning

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2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012

作者： Wang, Jun Woznica, Adam Kalousis, Alexandros AI Lab. Department of Computer Science University of Geneva Switzerland Department of Business Informatics University of Applied Sciences Switzerland

ISBN: (纸本)9783642334597

Metric learning methods have been shown to perform well on different learning tasks. Many of them rely on target neighborhood relationships that are computed in the original feature space and remain fixed throughout learning. As a result, the learned metric reflects the original neighborhood relations. We propose a novel formulation of the metric learning problem in which, in addition to the metric, the target neighborhood relations are also learned in a two-step iterative approach. The new formulation can be seen as a generalization of many existing metric learning methods. The formulation includes a target neighbor assignment rule that assigns different numbers of neighbors to instances according to their quality;'high quality' instances get more neighbors. We experiment with two of its instantiations that correspond to the metric learning algorithms LMNN and MCML and compare it to other metric learning methods on a number of datasets. The experimental results show state-of-the-art performance and provide evidence that learning the neighborhood relations does improve predictive performance. © 2012 Springer-Verlag.

关键词： Learning algorithms

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An architecture for designing Future Internet (FI) applications in sensitive domains: Expressing the software to data paradigm by utilizing hybrid cloud technology

An architecture for designing Future Internet (FI) applicati...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Stelios Sotiriadis Euripides G. M. Petrakis Stefan Covaci Paolo Zampognaro Eleni Georga Christoph Thuemmler Technical University of Crete (TUC) Intelligence Lab Chania Crete Greece Internet services platforms at the computer sciences and electrical engineering Institute for Telecommunication Systems R & D Lab of Engineering Ingegneria Informatica S.p.A. Rome Italy Department of Materials Science and Engineering University of Ioannina Ioannina Greece E-Health at the Institute for Informatics and Digital Innovation Technical University Munich Germany

The emergency of cloud computing and Generic Enablers (GEs) as the building blocks of Future Internet (FI) applications highlights new requirements in the area of cloud services. Though, due to the current restrictions of various certification standards related with privacy and safety of health related data, the utilization of cloud computing in such area has been in many instances unlawful. Here, we focus on demonstrating a “software to data” provisioning solution to propose a mapping of FI application use case requirements to software specifications (using GEs). The aim is to establish a provider to consumer cloud setting wherein no sensitive data will be exchanged but it will reside at the back-end site. We propose a prototype architecture that covers the cloud management layer and the operational features that manage data and Internet of Things devices. To show a real life scenario, we present the use case of the diabetes care and a FI application that includes various GEs.

关键词： Cloud computing Unified modeling language Sensors computer architecture Diabetes Personnel

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Interexchange museum database via web service

Interexchange museum database via web service

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2012 Annual SRII Global Conference, SRII 2012

作者： Kovavisaruch, L. Sornlertlamvanich, V. Kamolvej, P. Iamrahong, N. Prommoon, G. Digital Media Informatics Lab National Electronics and Computer Technology Center Pathumthani Thailand Dept. of Science Kasetsart University Bangkok Thailand

ISBN: (纸本)9780769547701

This paper is a conceptual presentation of a standardized museum database capable of exchanging information across various museum exhibitions in different countries. The design is a result of a study conducted on the database for cultural and artistic museum collections from multiple agencies. The study proposes a standardized database containing a collection of common, required information among museums, as well as additional related information. Implementation of the conceptual database design is expected to ensure important information is not lost during data exchange, allow remote database searches from one museum to another, and enable the exchange of cultural and artistic information internationally between countries that employ the same standard. © 2012 IEEE.

关键词： Electronic data interchange

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Adding regular expressions to graph reachability and pattern queries

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Frontiers of computer science 2012年第3期6卷 313-338页

作者： Wenfei FAN Jianzhong LI Shuai MA Nan TANG Yinghui WU School of Informatics University of EdinburghEdinburgh EH89YLUK Department of Computer Science and Engineering Harbin Institute of TechnologyHarbin 150001China NLSDE Lab Beihang UniversityBeijing 100919China Qatar Computing Research Institute Qatar FoundationDohaQatar

It is increasingly common to find graphs in which edges are of different types, indicating a variety of relation- ships. For such graphs we propose a class of reachability queries and a class of graph patterns, in which an edge is specified with a regular expression of a certain form, ex- pressing the connectivity of a data graph via edges of var- ious types. In addition, we define graph pattern matching based on a revised notion of graph simulation. On graphs in emerging applications such as social networks, we show that these queries are capable of finding more sensible informa- tion than their traditional counterparts. Better still, their in- creased expressive power does not come with extra complex- ity. Indeed, （1） we investigate their containment and mini- mization problems, and show that these fundamental prob- lems are in quadratic time for reachability queries and are in cubic time for pattern queries. （2） We develop an algorithm for answering reachability queries, in quadratic time as for their traditional counterpart. （3） We provide two cubic-time algorithms for evaluating graph pattern queries, as opposed to the NP-completeness of graph pattern matching via subgraph isomorphism. （4） The effectiveness and efficiency of these al- gorithms are experimentally verified using real-life data and synthetic data.

关键词： graph reachability graph pattern queries regu-lar expressions containment equivalence minimization

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Model-based multi-component volume data labeling and rendering 12

Model-based multi-component volume data labeling and renderi...

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SIGGRAPH Asia 2012 Posters, SA 2012

作者： Shen, Enya Xia, Jiazhi Cheng, Zhi-Quan Martin, Ralph R. Wang, Yunhai Li, Sikun School of Computer National University of Defense Technology China Central South University China School of Computer Science and Informatics Cardiff University United Kingdom Shenzhen VisuCA Key Lab/SIAT China

ISBN: (纸本)9781450319119

We present an intuitive multi-component volume data labeling and rendering system, driven by a given annotated semantic model (bought from [PlasticboyCC 2012]). Our system is based on a key idea: a user can obtain a reasonable specification for segmentation, labeling and color mapping by inferring the information from the provided model. This furthermore reduces the degree to which the user needs to be a domain expert, or visualization expert, himself. The input volume data may vary in detail from the model, and it is not annotated with semantic information, so making use of the semantic data still remains a challenging task. Our approach comprises three main steps. Copyright is held by the author / owner(s).

关键词： Semantics

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